Table 4

Global clustering test results with different adjustments.

Data/Model

Cuzick-Edwards k-NN

Moran's I

Tango's MEET

k = 1%

k= 1%

Test Statistic

p-value

Test Statistic

p-value

Test Statistic

p-value

Later Stage

1:Unadjusted data

173544

0.001

0.779

0.001

<10^-15

0.0001

2:Individual-level adjustments; no area-level random effects

170735

0.001

0.672

0.001

3.89 × 10^-15

0.0001

3:Individual-level adjustments; area-level random effects

170899

0.001

0.675

0.001

3.44 × 10^-15

0.0001

4:Individual- and area-level adjustments; no area-level random effects

168227

0.001

0.483

0.001

2.18 × 10^-08

0.0001

5:Individual- and area-level adjustments; area-level random effects

168684

0.001

0.489

0.001

7.17 × 10^-10

0.0001

Higher Grade

1:Unadjusted data

197019

0.001

0.478

0.001

1.36 × 10^-13

0.0001

2:Individual-level adjustments; no area-level random effects

195707

0.001

0.350

0.001

4.11 × 10^-08

0.0001

3:Individual-level adjustments; area-level random effects

195727

0.001

0.351

0.001

4.31 × 10^-08

0.0001

4:Individual- and area-level adjustments; no area-level random effects

195085

0.001

0.355

0.001

2.18 × 10^-08

0.0001

5:Individual- and area-level adjustments; area-level random effects

197598

0.001

0.701

0.001

<10^-15

0.0001

Higher values of the test statistic indicate more clustering for Cuzick-Edward's k-NN and Moran's I and less clustering for Tango's MEET. Test statistics can only be compared between models, not between the three methods.

Hinrichsen et al. International Journal of Health Geographics 2009 8:41   doi:10.1186/1476-072X-8-41